import itertools
from typing import Sequence, Union
import numpy as np
from pystencils import Field
from pystencils.datahandling.datahandling_interface import DataHandling
from pystencils.field import layout_string_to_tuple, spatial_layout_string_to_tuple, create_numpy_array_with_layout
from pystencils.parallel.blockiteration import SerialBlock
from pystencils.slicing import normalize_slice, remove_ghost_layers
from pystencils.utils import DotDict
try:
import pycuda.gpuarray as gpuarray
import pycuda.autoinit
except ImportError:
gpuarray = None
class SerialDataHandling(DataHandling):
def __init__(self, domain_size: Sequence[int], default_ghost_layers: int = 1, default_layout: str='SoA',
periodicity: Union[bool, Sequence[bool]] = False, default_target: str = 'cpu') -> None:
"""
Creates a data handling for single node simulations.
Args:
domain_size: size of the spatial domain as tuple
default_ghost_layers: default number of ghost layers used, if not overridden in add_array() method
default_layout: default layout used, if not overridden in add_array() method
default_target: either 'cpu' or 'gpu' . If set to 'gpu' for each array also a GPU version is allocated
if not overwritten in add_array, and synchronization functions are for the GPU by default
"""
super(SerialDataHandling, self).__init__()
self._domainSize = tuple(domain_size)
self.default_ghost_layers = default_ghost_layers
self.default_layout = default_layout
self._fields = DotDict()
self.cpu_arrays = DotDict()
self.gpu_arrays = DotDict()
self.custom_data_cpu = DotDict()
self.custom_data_gpu = DotDict()
self._custom_data_transfer_functions = {}
if periodicity is None or periodicity is False:
periodicity = [False] * self.dim
if periodicity is True:
periodicity = [True] * self.dim
self._periodicity = periodicity
self._field_information = {}
self.default_target = default_target
@property
def dim(self):
return len(self._domainSize)
@property
def shape(self):
return self._domainSize
@property
def periodicity(self):
return self._periodicity
@property
def fields(self):
return self._fields
def ghost_layers_of_field(self, name):
return self._field_information[name]['ghost_layers']
def values_per_cell(self, name):
return self._field_information[name]['values_per_cell']
def add_array(self, name, values_per_cell=1, dtype=np.float64, latex_name=None, ghost_layers=None, layout=None,
cpu=True, gpu=None, alignment=False):
if ghost_layers is None:
ghost_layers = self.default_ghost_layers
if layout is None:
layout = self.default_layout
if gpu is None:
gpu = self.default_target == 'gpu'
kwargs = {
'shape': tuple(s + 2 * ghost_layers for s in self._domainSize),
'dtype': dtype,
}
self._field_information[name] = {
'ghost_layers': ghost_layers,
'values_per_cell': values_per_cell,
'layout': layout,
'dtype': dtype,
}
if values_per_cell > 1:
kwargs['shape'] = kwargs['shape'] + (values_per_cell,)
index_dimensions = 1
layout_tuple = layout_string_to_tuple(layout, self.dim + 1)
else:
index_dimensions = 0
layout_tuple = spatial_layout_string_to_tuple(layout, self.dim)
# cpu_arr is always created - since there is no create_pycuda_array_with_layout()
byte_offset = ghost_layers * np.dtype(dtype).itemsize
cpu_arr = create_numpy_array_with_layout(layout=layout_tuple, alignment=alignment,
byte_offset=byte_offset, **kwargs)
cpu_arr.fill(np.inf)
if alignment and gpu:
raise NotImplementedError("Alignment for GPU fields not supported")
if cpu:
if name in self.cpu_arrays:
raise ValueError("CPU Field with this name already exists")
self.cpu_arrays[name] = cpu_arr
if gpu:
if name in self.gpu_arrays:
raise ValueError("GPU Field with this name already exists")
self.gpu_arrays[name] = gpuarray.to_gpu(cpu_arr)
assert all(f.name != name for f in self.fields.values()), "Symbolic field with this name already exists"
self.fields[name] = Field.create_from_numpy_array(name, cpu_arr, index_dimensions=index_dimensions)
self.fields[name].latex_name = latex_name
return self.fields[name]
def add_custom_data(self, name, cpu_creation_function,
gpu_creation_function=None, cpu_to_gpu_transfer_func=None, gpu_to_cpu_transfer_func=None):
if cpu_creation_function and gpu_creation_function:
if cpu_to_gpu_transfer_func is None or gpu_to_cpu_transfer_func is None:
raise ValueError("For GPU data, both transfer functions have to be specified")
self._custom_data_transfer_functions[name] = (cpu_to_gpu_transfer_func, gpu_to_cpu_transfer_func)
assert name not in self.custom_data_cpu
if cpu_creation_function:
assert name not in self.cpu_arrays
self.custom_data_cpu[name] = cpu_creation_function()
if gpu_creation_function:
assert name not in self.gpu_arrays
self.custom_data_gpu[name] = gpu_creation_function()
def has_data(self, name):
return name in self.fields
def add_array_like(self, name, name_of_template_field, latex_name=None, cpu=True, gpu=None):
return self.add_array(name, latex_name=latex_name, cpu=cpu, gpu=gpu,
**self._field_information[name_of_template_field])
def iterate(self, slice_obj=None, gpu=False, ghost_layers=True, inner_ghost_layers=True):
if ghost_layers is True:
ghost_layers = self.default_ghost_layers
elif ghost_layers is False:
ghost_layers = 0
elif isinstance(ghost_layers, str):
ghost_layers = self.ghost_layers_of_field(ghost_layers)
if slice_obj is None:
slice_obj = (slice(None, None, None),) * self.dim
slice_obj = normalize_slice(slice_obj, tuple(s + 2 * ghost_layers for s in self._domainSize))
slice_obj = tuple(s if type(s) is slice else slice(s, s + 1, None) for s in slice_obj)
arrays = self.gpu_arrays if gpu else self.cpu_arrays
custom_data_dict = self.custom_data_gpu if gpu else self.custom_data_cpu
iter_dict = custom_data_dict.copy()
for name, arr in arrays.items():
field_gls = self._field_information[name]['ghost_layers']
if field_gls < ghost_layers:
continue
arr = remove_ghost_layers(arr, index_dimensions=len(arr.shape) - self.dim,
ghost_layers=field_gls - ghost_layers)
iter_dict[name] = arr
offset = tuple(s.start - ghost_layers for s in slice_obj)
yield SerialBlock(iter_dict, offset, slice_obj)
def gather_array(self, name, slice_obj=None, ghost_layers=False, **kwargs):
gl_to_remove = self._field_information[name]['ghost_layers']
if isinstance(ghost_layers, int):
gl_to_remove -= ghost_layers
if ghost_layers is True:
gl_to_remove = 0
arr = self.cpu_arrays[name]
ind_dimensions = self.fields[name].index_dimensions
spatial_dimensions = self.fields[name].spatial_dimensions
arr = remove_ghost_layers(arr, index_dimensions=ind_dimensions, ghost_layers=gl_to_remove)
if slice_obj is not None:
normalized_slice = normalize_slice(slice_obj[:spatial_dimensions], arr.shape[:spatial_dimensions])
normalized_slice = tuple(s if type(s) is slice else slice(s, s + 1, None) for s in normalized_slice)
normalized_slice += slice_obj[spatial_dimensions:]
arr = arr[normalized_slice]
else:
arr = arr.view()
arr.flags.writeable = False
return arr
def swap(self, name1, name2, gpu=False):
if not gpu:
self.cpu_arrays[name1], self.cpu_arrays[name2] = self.cpu_arrays[name2], self.cpu_arrays[name1]
else:
self.gpu_arrays[name1], self.gpu_arrays[name2] = self.gpu_arrays[name2], self.gpu_arrays[name1]
def all_to_cpu(self):
for name in (self.cpu_arrays.keys() & self.gpu_arrays.keys()) | self._custom_data_transfer_functions.keys():
self.to_cpu(name)
def all_to_gpu(self):
for name in (self.cpu_arrays.keys() & self.gpu_arrays.keys()) | self._custom_data_transfer_functions.keys():
self.to_gpu(name)
def run_kernel(self, kernel_function, *args, **kwargs):
data_used_in_kernel = [p.field_name
for p in kernel_function.parameters if
p.is_field_ptr_argument and p.field_name not in kwargs]
arrays = self.gpu_arrays if kernel_function.ast.backend == 'gpucuda' else self.cpu_arrays
array_params = {name: arrays[name] for name in data_used_in_kernel}
array_params.update(kwargs)
kernel_function(*args, **array_params)
def to_cpu(self, name):
if name in self._custom_data_transfer_functions:
transfer_func = self._custom_data_transfer_functions[name][1]
transfer_func(self.custom_data_gpu[name], self.custom_data_cpu[name])
else:
self.gpu_arrays[name].get(self.cpu_arrays[name])
def to_gpu(self, name):
if name in self._custom_data_transfer_functions:
transfer_func = self._custom_data_transfer_functions[name][0]
transfer_func(self.custom_data_gpu[name], self.custom_data_cpu[name])
else:
self.gpu_arrays[name].set(self.cpu_arrays[name])
def is_on_gpu(self, name):
return name in self.gpu_arrays
def synchronization_function_cpu(self, names, stencil_name=None, **_):
return self.synchronization_function(names, stencil_name, 'cpu')
def synchronization_function_gpu(self, names, stencil_name=None, **_):
return self.synchronization_function(names, stencil_name, 'gpu')
def synchronization_function(self, names, stencil=None, target=None, **_):
if target is None:
target = self.default_target
assert target in ('cpu', 'gpu')
if not hasattr(names, '__len__') or type(names) is str:
names = [names]
filtered_stencil = []
neighbors = [-1, 0, 1]
if (stencil is None and self.dim == 2) or (stencil is not None and stencil.startswith('D2')):
directions = itertools.product(*[neighbors] * 2)
elif (stencil is None and self.dim == 3) or (stencil is not None and stencil.startswith('D3')):
directions = itertools.product(*[neighbors] * 3)
else:
raise ValueError("Invalid stencil")
for direction in directions:
use_direction = True
if direction == (0, 0) or direction == (0, 0, 0):
use_direction = False
for component, periodicity in zip(direction, self._periodicity):
if not periodicity and component != 0:
use_direction = False
if use_direction:
filtered_stencil.append(direction)
result = []
for name in names:
gls = self._field_information[name]['ghost_layers']
if len(filtered_stencil) > 0:
if target == 'cpu':
from pystencils.slicing import get_periodic_boundary_functor
result.append(get_periodic_boundary_functor(filtered_stencil, ghost_layers=gls))
else:
from pystencils.gpucuda.periodicity import get_periodic_boundary_functor
result.append(get_periodic_boundary_functor(filtered_stencil, self._domainSize,
index_dimensions=self.fields[name].index_dimensions,
index_dim_shape=self._field_information[name][
'values_per_cell'],
dtype=self.fields[name].dtype.numpy_dtype,
ghost_layers=gls))
if target == 'cpu':
def result_functor():
for arr_name, func in zip(names, result):
func(pdfs=self.cpu_arrays[arr_name])
else:
def result_functor():
for arr_name, func in zip(names, result):
func(pdfs=self.gpu_arrays[arr_name])
return result_functor
@property
def array_names(self):
return tuple(self.fields.keys())
@property
def custom_data_names(self):
return tuple(self.custom_data_cpu.keys())
def reduce_float_sequence(self, sequence, operation, all_reduce=False) -> np.array:
return np.array(sequence)
def reduce_int_sequence(self, sequence, operation, all_reduce=False) -> np.array:
return np.array(sequence)
def create_vtk_writer(self, file_name, data_names, ghost_layers=False):
from pystencils.vtk import image_to_vtk
def writer(step):
full_file_name = "%s_%08d" % (file_name, step,)
cell_data = {}
for name in data_names:
field = self._get_field_with_given_number_of_ghost_layers(name, ghost_layers)
if self.dim == 2:
field = field[:, :, np.newaxis]
if len(field.shape) == 3:
cell_data[name] = np.ascontiguousarray(field)
elif len(field.shape) == 4:
values_per_cell = field.shape[-1]
if values_per_cell == self.dim:
field = [np.ascontiguousarray(field[..., i]) for i in range(values_per_cell)]
if len(field) == 2:
field.append(np.zeros_like(field[0]))
cell_data[name] = tuple(field)
else:
for i in range(values_per_cell):
cell_data["%s[%d]" % (name, i)] = np.ascontiguousarray(field[..., i])
else:
assert False
image_to_vtk(full_file_name, cell_data=cell_data)
return writer
def create_vtk_writer_for_flag_array(self, file_name, data_name, masks_to_name, ghost_layers=False):
from pystencils.vtk import image_to_vtk
def writer(step):
full_file_name = "%s_%08d" % (file_name, step,)
field = self._get_field_with_given_number_of_ghost_layers(data_name, ghost_layers)
if self.dim == 2:
field = field[:, :, np.newaxis]
cell_data = {name: np.ascontiguousarray(np.bitwise_and(field, field.dtype.type(mask)) > 0, dtype=np.uint8)
for mask, name in masks_to_name.items()}
image_to_vtk(full_file_name, cell_data=cell_data)
return writer
def _get_field_with_given_number_of_ghost_layers(self, name, ghost_layers):
actual_ghost_layers = self.ghost_layers_of_field(name)
if ghost_layers is True:
ghost_layers = actual_ghost_layers
gl_to_remove = actual_ghost_layers - ghost_layers
ind_dims = 1 if self._field_information[name]['values_per_cell'] > 1 else 0
return remove_ghost_layers(self.cpu_arrays[name], ind_dims, gl_to_remove)